Total Heat Demand Research Articles

Covering District Heating Demand with Waste Heat from Data Centres

Currently, the huge potential of data centre waste heat for the decarbonization of the heating sector is often ignored. Here, a feasibility study is presented for covering the heat demand of two districts of Frankfurt (Germany) mainly by data centre waste heat. Contrary to many existing projects, the waste heat shall supply existing buildings, the total heat demand is very high (144 GWh/a) and it should be covered almost exclusively by waste heat usage. In this study, the potential and demand are estimated, a heating supply concept is presented and evaluated regarding costs, heat pump capacity, and storage size. As a result, the utilisation of data centre waste heat is not only possible, but it’s the most promising way for decarbonizing the heating sector in this area. With high-capacity heat pumps (37 MWth), gas boilers (20 MWth) and a storage for daily peaks, 97.5 % of the heat demand can be covered by waste heat usage. It is economically favourable compared to decentralized heat pumps for all types of buildings and with the proposed concept, CO2-emissions in the network area are reduced by 78 % on average. Laws to oblige the waste heat usage of data centres are recommended.

From Retrofitting to Renewables: Navigating Energy Transition Pathways for European Residential Space Heating

Transformative actions are crucial across all sectors emitting greenhouse gases. Nonetheless, energy transition research to date displays a notable imbalance, with a larger emphasis on the supply side than on the demand side. The present study addresses this inequity by focusing on residential sector space heating demand, a frequently overlooked energy service that currently contributes substantially to global greenhouse gas emissions. Our primary objective is to pinpoint effective climate policies and space heating strategies that align with the EU’s ambitious targets for emission reduction. We employ the recently developed TIMES-Europe model to conduct a comprehensive analysis of the residential sector’s policy frameworks, technological advancements, and associated costs. This analysis aims to determine the measures necessary to meet ambitious climate objectives within the European context. To achieve this, we formulate four distinct scenarios, each representing varying levels of ambition and collaboration among EU member states, thereby providing insight into the pathways toward achieving these targets. By implementing current intended EU policies on the renovation of dwellings, we project a substantial reduction of at most 850 PJ, or, i.e., a 19% decrease, in yearly energy demand for space heating between 2020 and 2050. In contrast, if the recent pace of dwelling renovation within the EU were to continue, space heating energy savings from renovation would only amount to less than 400 PJ/yr (i.e., a 9% reduction) in the same period. In our more ambitious climate scenarios, phasing out fossil fuels leads to widespread electrification of the European residential sector, and by 2050, electricity from heat pumps and electric boilers accounts for over 68% of the total residential sector space heating demand. The outcomes of our study underline the importance of implementing the currently planned EU policies. We also demonstrate the necessity for collaboration among EU member states in order to attain the common European climate targets under the most effective resource allocation.

Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data

Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy is still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. Therefore, policymakers increasingly strive to trigger investments in sustainable and low-emission heating systems. This study forms part of the “Roll-out of Deep Geothermal Energy in North-West-Europe”-project and aims at quantifying the spatial heat demand distribution in the Interreg North-West-Europe region. An open-source geographic information system and selected Python packages for advanced geospatial processing, analysis, and visualization are utilized to constrain the maps. These were combined, streamlined, and optimized within the open-source Python package PyHeatDemand. Based on national and regional heat demand input data, three maps are developed to better constrain heat demand at a high spatial resolution of 100 m × 100 m (=1 ha) for the residential and commercial sectors, and for both together (in total). The developed methodology can not only be applied to transnational heat demand mapping but also on various scales ranging from city district level to states and countries. In addition, the workflow is highly flexible working with raster data, vector data, and tabular data. The results reveal a total heat demand of the Interreg North-West-Europe region of around 1700 TWh. The spatial distribution of the heat demand follows specific patterns, where heat demand peaks are usually in metropolitan regions like for the city of Paris (1400 MWh/ha), the city of Brussels (1300 MWh/ha), the London metropolitan area (520 MWh/ha), and the Rhine-Ruhr region (500 MWh/ha). The developed maps are compared with two international projects, Hotmaps and Heat Roadmap Europe’s Pan European Thermal Atlas. The average total heat demand difference from values obtained in this study to Hotmaps and Heat Roadmap Europe is 24 MWh/ha and 84 MWh/ha, respectively. Assuming the implementation of real consumption data, an enhancement in spatial predictability is expected. The heat demand maps are therefore predestined to provide a conceptual first overview for decision-makers and market investors. The developed methods will further allow for anticipated mandatory municipal heat demand analyses.

Model predictive control of bidirectional heat transfer in prosumer-based solar district heating networks

District heating networks with decentralized heat production are ideally suited to include a high share of renewable energy sources for heat production in urban areas with limited space. A new concept is a prosumer-based district heating network in which some or even all buildings are equipped with decentralized building-level heat storages and heat generation plants. To exploit the full potential of the interconnected heating network, buildings with surplus heat are intended to transfer heat to buildings with heating demands to prevent the activation of the central heating plant. This work presents an initial feasibility study on using a model predictive control strategy to manage bidirectional heat transfer between buildings in a district heating network. We formulate this problem as an optimal control problem, incorporating binary decision variables for each potential heat transfer connection. This results in a mixed-integer nonlinear optimization problem that is hard to solve. The problem is solved with a fast gradient-based optimization algorithm in combination with a combinatorial integral approximation strategy. A case study concerning a residential heating network with building-level solar thermal collectors and storage tanks is carried out. The optimized operation is compared to the real operation obtained from one month of measurements. The results show that the optimized strategy with bidirectional heat transfer can exploit the total heat generated in the whole network. The central heating provider is only required when the heat produced or stored by all the buildings is insufficient to meet the total heat demand within the network. In the case study, this leads to a reduction of almost 75% of the required thermal energy from the central heat provider.

Energy, exergy, environment and techno-economic analysis of hybrid solar-biomass systems for space heating and hot water supply: Case study of a Hammam building

The main goal of this study is to assess the thermodynamic performance, environmental benefits and economic feasibility of using a new hybrid solar-biomass system (HSBS) for the production of domestic hot water at 60 °C and for the provision of space heating via an underfloor heating system in a Hammam building located in Marrakesh-Morocco. The HSBSs consists of small biomass pellet boilers in combination with three types of solar thermal systems (evacuated tube, flat plate and parabolic trough collectors). A TRNSYS simulation model of a biomass heating system was developed and validated against experimental data. Performance comparisons of the biomass system with various optimal HSBSs were then conducted, and their results evaluated in terms of energy, exergy, environmental, and economic perspectives. Comparisons between various collectors’ technology revealed that the optimum collector areas of 204 m2, 224 m2 and 300 m2 are responsible for the lowest Levelized Cost of Heat (LCOH) corresponding to about 0.0755 $/kWh, 0.0696 $/kWh and 0.0642 $/kWh when using PTC, FPC and ETC, respectively. Furthermore, the optimal HSBS with evacuated tube collectors is the most effective configuration with a solar fraction of 57 %, and a total life-cycle saving cost of 0.509 M$ for a total annual heat demand of 533 MWh. Moreover, the total energy and exergy efficiencies are calculated equal to 40 % and 3.9 %, respectively. The payback period is found to be around 4.9 years, the levelized cost of heat 0.0642 $/kWh, while the annual CO2 avoidance amounts to 656 tons.

Automatic Identification and Evaluation of Building Clusters for Optimal District Heating Networks Phasing

Considering future city changes when designing district heating networks can be tedious. This paper tackles this problem by automatically identifying potential future building clusters connections, and evaluating relevant financial and planning criteria. The methodology uses open GIS data for cluster identification and building heat demand estimation, and thermal-hydraulic models for simulating the network. On a neighbourhood scale, area characteristics such as total heat demand, distance to source, and road surface type mainly affect the decision of where to expand the network to. However, on a street scale, the sizing and location of the initial network mainly affect the network’s phasing.

A data-driven model for the analysis of energy consumption in buildings

Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregation algorithm is proposed to distinguish space heating from domestic hot water usage. The method also evaluates the building’s thermal inertia, examining the correlation between the hourly global energy consumption and the outdoor air temperature moving average. It also presents a methodology for improving the DHW heat consumption model. The methodology is applied to a case study of 51 buildings in Tartu, Estonia, with complete yearly demand measurements from the district heating operator. Thanks to the hourly calibration approach, R2 is 0.05 higher on average than the yearly Energy Signature Curve approach. The difference between estimated and measured annual energy consumption is 8% on average, demonstrating the practicality and effectiveness of the proposed method.

Data-based, high spatiotemporal resolution heat pump demand for power system planning

Decarbonizing the residential building sector by replacing gas boilers with electric heat pumps will dramatically increase electricity demand. Existing models of future heat pump demand either use daily heating demand profiles that do not capture heat pump use or do not represent sub-national heating demand variation. This work presents a novel method to generate high spatiotemporal resolution residential heat pump demand profiles based on heat pump field trial data. These spatially varied demand profiles are integrated into a generation, storage, and transmission expansion planning model to assess the impact of spatiotemporal variations in heat pump demand. This method is demonstrated and validated using the British power system in the United Kingdom (UK), and the results are compared with those obtained using spatially uniform demand profiles. The results show that while spatially uniform heating demand can be used to estimate peak and total annual heating demand and grid-wide systems cost, high spatiotemporal resolution heating demand data is crucial for spatial power system planning. Using spatially uniform heating demand profiles leads to 15.1 GW of misplaced generation and storage capacity for a 90% carbon emission reduction from 2019. For a 99% reduction in carbon emissions, the misallocated capacity increases to 16.9-23.9 GW. Meeting spatially varied heating load with the system planned for uniform national heating demand leads to 5% higher operational costs for a 90% carbon emission reduction. These results suggest that high spatiotemporal resolution heating demand data is especially important for planning bulk power systems with high shares of renewable generation.

Long-term numerical investigation of Northern Gateway Heat Network

Considering climate change and countries’ measures towards greenhouse gas emissions (e.g., European energy and climate targets of achieving net-zero greenhouse gas emissions by 2050) [1], it is crucial to implement renewable energy sources and district-level heating and cooling systems. Many district-level heating and cooling systems have been established in Europe. However, just a limited number of them are renewable energy source based. One of the most efficient ways of heating and cooling supply is to use geothermal energy as it is renewable, sustainable, and environmentally friendly. Groundwater heat pumps (GWHPs) utilise low-temperature sources to provide heating and cooling to buildings, which makes them a highly efficient, environmentally friendly, and low-carbon technology suitable for both small and large-scale applications.
 Northern Gateway Heat Network project, which is a groundwater-based heat pump system aiming to provide heating and domestic hot water to around 300 dwellings, offices, and healthcare buildings, is considered as a case study in this research. It was designed to supply 75% of the total heating demand with an 800-kW output heat pump benefitting from the groundwater extracted from the depths between 135m and 200m at around 12.5°C. BH1, BH2 and BH3 are the injection wells, whilst BH4 and BH5 are the abstraction wells (see Figure 1) [2, 3].
 A 3D finite element modelling was carried out in FEFLOW [4] to investigate the thermally affected zone and the possibility of thermal feedback resulting from water injection temperature affecting abstraction temperature. The 3D modelling aims at investigating the impact of the system in the long term (100 years) and how to improve the system’s efficiency and sustainability by considering four different scenarios: (1) continuous space heating (12 months), (2) space heating (10 months) and recovery (2
 months), (3) space heating (10 months) and cooling (2 months) and (4) aquifer thermal energy storage (swap injection and abstraction wells in 2 months cooling period) (ATES).
 The results suggest that thermal feedback occurs after almost 60 years for the actual case (continuous space heating), which decreases the system’s efficiency. Figure 1 illustrates the thermal plume development after 100 years of space heating operation (cold water injection at 5°C). Although the spacing between the production and injection wells is around 530 m, the abstraction temperature was affected by the cold-water injection, and it decreased by almost 10% at the end of 100 years of simulation at BH4.
 The space heating and recovery, and space heating and cooling also witnessed a decrease in water extraction temperature at BH4. The injection temperature was set to 35°C for the space cooling period. Adding two months of recovery or two months of space cooling delayed the thermal feedback time by 17% and 8%. The results show that the system can supply two months of space cooling without a need for an additional supply from the heat pump. Furthermore, when two months of space cooling is added, the thermal energy gain increases by 20% compared to space heating-only operation. The ATES operation also increased the thermal energy gain by 173% thanks to the stored energy in the ground and higher volume of water extraction during the space cooling period. Table 1 shows the thermal energy gain from each well in each scenario, calculated considering the thermal feedback impact.
 This paper highlights the long-term numerical investigation of the Northern Gateway Heat Network, considering different scenarios, including continuous space heating, space heating and recovery, space heating and cooling, and ATES. It also discusses the thermal impact and thermal plume development in different scenarios and how the thermal plume affected the system efficiency due to thermal feedback. The results indicate that the GWHP system can provide direct space cooling, thereby significantly increasing the sustainability and efficiency of the system. Moreover, the application of the ATES system further improves the system’s sustainability and efficiency.

Techno-economic assessment from a transient simulation of a concentrated solar thermal plant to deliver high-temperature industrial process heat

The need for new technologies to provide economically viable sources of high-temperature heat without any net CO2 emissions for industrial chemical processes has become a global priority. Concentrated solar thermal heat generation can play an important role in meeting this challenge. We report a detailed bottom-up techno-economic analysis of a concentrated solar thermal plant to deliver process heat at a temperature of 1100 °C at the scale anticipated for a commercial demonstrator. The system consists of a 50 MWth solar concentration subsystem with heliostats and a tower, a novel solar expanding-vortex particle receiver (SEVR) and a packed bed storage system, together with a combustion back-up to allow continuous operation. The system was optimized to determine the minimum levelized cost of heat (LCOH) from a detailed transient model of the complete system. Significantly, the system with the maximum efficiency for the receiver or storage alone does not deliver the best annual energetic performance, highlighting the need to understand the detailed interplay between the two systems to optimize overall performance. While this system is not yet optimized for the lowest cost of industrial heat, the sensitivity analysis provides important insight as to how to move toward this optimum. This system yields the lowest values of LCOH for the solar component of between 37 and 39 USD/GJ for a corresponding annual solar share (SSann) of 28% and 53% of the total heat demand of the industrial process, respectively. However, importantly, this solar share can be almost doubled for only a 5% increase in LCOH.

A numerical study on performance efficiency of a low-temperature horizontal ground-source heat pump system

Exploitation of shallow ground and its low-grade heat potential is fundamental to designing 5th generation district heating and cooling (DHC) networks. Horizontal ground-source heat pump (HGSHP) systems are a common way to utilize shallow geothermal energy. Realistic estimation and prediction of performance of a HGSHP system and shallow ground thermal behaviour should consider the whole system including building heating and cooling load, heat pump and ground heat exchanger, and the ground. This should be accompanied by realistic atmospheric and ground conditions. In this paper, a three-dimensional coupled thermal–hydraulic model with realistic boundary conditions adopting a whole system approach is presented. Dynamic heat pump coefficient of performance (COP) that depends on seasonal variation of heating/cooling demand and ground conditions are also considered. Model validations are conducted against experimental and analytical results in literatures. The model is applied for evaluating a HGSHP system to support development of a 5th generation DHC network on a potential site in the UK. Several influencing factors, such as ground moisture transfer, building thermal load mode, buried depth of ground loops, and initial ground temperature profile are studied to assess performance efficiency of the HGSHP system and evolution of ground thermal behaviour in response to heat extraction or rejection into the ground. The results show that 5% of the monthly total heat demand of the site could be met by the designed HGSHP system, consisting of 200 U-shaped ground loops buried at the depth of 3 m and pure water as the heat carrier. Overlooking the ground moisture transfer or hyperbolizing the ground saturation would overestimate the load-carrying capacity of the HGSHP system. The HGSHP system is more efficient with a higher heat pump COP under the heating and cooling mode than under the heating-only mode. Predicted performance of the HGSHP system improves with buried depth of the ground loops. The results also show that a 1 ℃ increase in the undisturbed ground temperature could suffice up to 8% of the monthly total heat demand of the site.

Investigating the heating and air conditioning provision capability of a supermarket to neighboring buildings: Field measurement analysis and economic evaluation

This study explores the capability of a supermarket to fulfill the thermal demands of neighboring buildings. The research combines field measurements analysis, obtained from a Swedish supermarket which covers the neighbors' thermal demands, with theoretical calculations, using thermodynamic models of the refrigeration system and the ground source thermal storage. In contrast to previous studies, this work is unique in its use of demonstrating a real-life built case study rather than only theoretical approach. Results demonstrate that integrating ground source storage in a supermarket can meet the thermal demands of neighboring buildings without requiring additional equipment. The refrigeration system accounts for 80% of the total heat demand, while the ground serves as a heat source for the rest. This approach results in 67% annual operating cost savings for the neighboring buildings. In addition, ground source subcooling provides 9% annual energy savings to supermarket's refrigeration system. By exporting heat to neighboring buildings, the proposed solution emits 90% less CO2 in Uppsala and 67% less in Stockholm compared to solutions in which the refrigeration system only meets the refrigeration demands. Summing up, the concept of utilizing supermarkets to meet thermal demands offers a more energy-efficient operation for both supermarkets and property owners while significantly reducing annual energy consumption and emissions.

Multicriteria Analysis of a Solar-Assisted Space Heating Unit with a High-Temperature Heat Pump for the Greek Climate Conditions

The goal of this investigation is the thorough analysis and optimization of a solar-assisted heat pump heating unit for covering the space heating demand for a building in Athens, Greece. The novelty of the studied system is the use of a high-temperature heat pump that can operate with radiative terminal units, leading to high thermal comfort standards. The examined system includes flat-plate solar thermal collectors, an insulated thermal storage tank, auxiliary electrical thermal resistance in the tank and a high-temperature heat pump. The economic optimization indicates that the optimal design includes 35 m2 of solar thermal collectors connected with a storage tank of 2 m3 for facing the total heating demand of 6785 kWh. In this case, the life cycle cost was calculated at 22,694 EUR, the seasonal system coefficient of performance at 2.95 and the mean solar thermal efficiency at 31.60%. On the other hand, the multi-objective optimization indicates the optimum design is the selection of 50 m2 of solar field connected to a thermal tank of 3 m3. In this scenario, the life cycle cost was calculated at 24,084 EUR, the seasonal system coefficient of performance at 4.07 and the mean solar thermal efficiency at 25.33%.

Energy analysis and performance assessment of a hybrid deep borehole heat exchanger heating system with direct heating and coupled heat pump approaches

The deep borehole heat exchanger (DBHE) coupled ground source heat pump (GSHP) system is widely developed in recent years to extract geothermal energy for building heating. However, its operating mechanism and energy flows within different components have not been comprehensively investigated. Meanwhile, the high outlet temperature of DBHE has the potential to meet the direct heating requirement but may exceed the GSHP’s temperature threshold. Therefore, this study first presented a hybrid DBHE heating system transient model with direct heating and coupled GSHP approaches using TRNSYS and MATLAB software. The novelty of this system is that DBHE was also coupled with a heat exchanger (HEX) to realize direct heating. Taking a residential building in Xi’an and DBHE with the depth of 2000 m as a scenario, the operation behavior and energy performance of the system were analyzed. The results showed that HEX will operate for several days before operating GSHP at the beginning of heating season, and annual HEX operating time gradually decreases from 27.1 days to 8.8 days over 10 years. Annual energy fraction of heat extracted from the soil by the HEX and GSHP in the total heating demand gradually decreases from 0.14 to 0.05 and increases from 0.72 to 0.78 over 10 years, respectively. Further, the importance of HEX in system performance was evaluated. Compared with the hybrid system, the GSHP’s COP will be overestimated by 11.3 % and the heat extraction amount will be significantly underestimated when HEX is not designed in the DBHE system. Moreover, the effects of different building heating load characteristics (including actual load and constant load) on system performance were discussed. It was found that the minimum inlet temperature of DBHE during the heating season under constant load will be overestimated, with a maximum deviation of 8.4 °C. Dynamic building load also results in a parabolic soil temperature isotherm at the end of heating season.

An assessment of the impacts of heat electrification on the electric grid in the UK

To achieve net zero emissions by 2050, the world economy needs to be significantly decarbonized. Among all sectors, the decarbonization of heat is likely to incorporate a significant electronification using electric heat pumps, placing additional strain on electric grid infrastructure in countries like the UK. In this paper, we present an assessment of the impacts of heat electrification on the grid in the UK in 2050 when the net zero is reached. A high-resolution heat demand forecasting model is developed to estimate hourly heat demand at the national level and it is validated using published historical data. The validated model is then used to predict the hourly heat demand of the UK in 2050. The impact of heat electrification is then analysed based on several different scenarios. The results show that heat decarbonization through electrification will have a significant impact on the grid. In the case of extreme global warming, both hourly peak demand and annual total heat demand will drop by about 45% in 2050, compared with published data in 2010. In the case of 100% electrification with no global warming, the total annual electricity demand for heating is 109.4 TWh. The hourly peak demand of electricity will increase from 96.6 GW to 146.7 GW, which requires an increase of electricity generation capacity by around 50%. It is however economically infeasible and unviable to build such a huge new generation capacity that would only be used in winter when the peak heat demand peaks but will be switched off during summer. Hence, mitigation measures such as heat storage will play a vital role in balancing power generation and consumption, as well as reducing the required new generation capacity.

Implementation of System Identification Techniques and Optimal Control for RC Model Selection by Means of TRNSYS Simulation Results and Experimental Data

Simulating the thermal model of a district requires simultaneously retaining accuracy and simplicity, in order to avoid cumbersome calculations and unrealistic predictions. Within this scope, introducing a simple structure for modeling the energy consumption of a building that can be expanded to the district level becomes essential. In this paper, a hierarchy of thermal models with increasing complexity is developed to identify the simplest structure that can effectively represent the thermal behavior of a building, using a simulated building in TRNSYS and the measurements of a real building as two datasets to estimate the model parameters. Each model is placed in a closed loop system to track the constant indoor temperature by means of the linear quadratic regulator (LQR) technique. To select the best structure, the model outputs are compared to TRNSYS simulations and measurements. The main features of the selected models include the use of only a few parameters to predict the indoor temperature, peak power, total heat demand, and transient behavior of a building. It is shown that the proposed models are able to determine the indoor temperature with less than 1 °C of error, and the total heat demand and peak power are also determined with an error lower than 25%.

Dataset level explanation of heat demand forecasting ANN with SHAP

This paper aims to provide a thorough guide on how to analyze complex energy demand forecasting models with Shapley Additive exPlanations (SHAP) in order to build trust in their predictions and understand the model and SHAP limitations based on selected real-world use case. There are only few examples described in the literature in energy industry and they present very basic usage. This study fills the gap for the class of energy (heat, electric, gas) demand predicting models by showing step by step, top-down analysis of state-of-the-art, deep neural network model predicting total heat demand (hot water and room heating) in Warsaw District Heating Network – the largest district heating network in EU. The paper shows how SHAP can be successfully used on demand forecasting models to provide practical, easily interpretable insights on inner workings of these models which can be used to assess their reliability and plan further development.

Transition towards university campus carbon neutrality by connecting to city district heating network

The campus of Tallinn University of Technology consists of 26 buildings with a total annual heat demand of approximately 20 GWh. A local natural gas-fired boiler provides annually approximately 13 GWh of heating to 12 buildings in the campus and 14 buildings are connected to district heating system. This paper analyses the possibilities of replacing the natural gas boiler with district heating. Two systems were modelled using EnergyPRO software and compared to the reference system of the local boiler and heating network: connection to an existing high-temperature district heating network and a low-temperature energy cascade. All the three systems were modelled with two different energy price scenarios. The results were analysed from the perspective of the university campus and the entire city’s system. The low-temperature energy cascade connection to the city’s network will reduce carbon dioxide emissions by 955 tonnes CO2. The conventional high-temperature connection would reduce the emission by 765 tons CO2. District heating connection will also lead to primary energy savings supporting the university’s efforts towards achieving its sustainable development goals. The low-temperature energy cascade utilising the return water of the city’s district heating network reduces the heat losses and increases the efficiency of heat and electricity production when compared to the systems with separate campus heating or the conventional high-temperature district heating.

Economical design optimization of a phase change material active system equipped with a solar collector

Coupling solar air collectors and active systems of phase change materials is an efficient approach for improving building heating supply. Through design optimization, such a system can become economical and energy-efficient in different climates and for different types of buildings. However, previous works have not paid much attention to the economic feasibility of such systems during designing. In this paper, economical design optimization of a system including a solar collector and a PCM heat exchanger was explored. For this purpose, a model that integrated the solar collector, PCM storage, and hut dynamics was developed for the first time. The average mean square error between the measured and predicted hut temperatures over 11 days was 4 °C. The validated model was then used for the design optimization of the system for three different scenarios: office, domestic, and service, with different schedule times and comfort temperatures in the cold season of Auckland. The results showed that the optimum surface area of the solar collector was the same at 1 square metre for all the scenarios; however, the optimum amounts of PCM mass for service, domestic, and office scenarios were 35 kg, 20 kg, and zero, respectively. The optimum values indicate that the system can be cost-effective when used as an auxiliary heating system to meet a portion of the total heating demand.

System modelling and optimization of a low temperature local hybrid energy system based on solar energy for a residential district

Utilizing solar energy for heat supply can reduce CO2 emissions and mitigate global climate change. In the Nordic region (e.g., Iceland and Finland), a tremendous seasonal mismatch exists between the availability of solar radiation and building heating demand. This paper proposes a local hybrid energy system based on solar energy for a residential district. It applies a borehole thermal energy storage to store solar energy in non-heating seasons, and uses stored energy for part of total heating demand in a residential neighbourhood in heating seasons. Photovoltaic panels are used to generate electricity for heat pump operation. To find out cost-optimal and eco-friendly solutions, the local energy system was first modelled and simulated in TRNSYS. Then, genetic algorithms were applied to optimize the system performance and costs. In optimal solutions, 38%-58% of total heating demand could be covered by on-site heat energy with the levelized cost of energy of 110–184 €/MWh. On this basis, importing additional electricity from grid to increase the utilization rate of air-to-water heat pumps can further increase the on-site heat energy fraction to 41%-88% with the levelized cost of energy of 108–201 €/MWh. Compared with the situation of fully district heating input, the proposed system can annually reduce CO2 emissions by 102–217 tons with the rate of 31–66%. Although the initial cost of the studied system is higher than that of district heating, the local hybrid energy system is worth further developing considering decentralizing heat energy production and reducing CO2 emissions.